Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1696240
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory562.2 MiB
Average record size in memory347.5 B

Variable types

Text2
Numeric7
DateTime1
Categorical3

Alerts

game_id is highly overall correlated with player_idHigh correlation
player_club_id is highly overall correlated with player_current_club_idHigh correlation
player_current_club_id is highly overall correlated with player_club_idHigh correlation
player_id is highly overall correlated with game_idHigh correlation
yellow_cards is highly imbalanced (60.9%) Imbalance
red_cards is highly imbalanced (96.4%) Imbalance
appearance_id has unique values Unique
goals has 1550722 (91.4%) zeros Zeros
assists has 1577536 (93.0%) zeros Zeros

Reproduction

Analysis started2025-03-12 19:10:34.392723
Analysis finished2025-03-12 19:11:10.494902
Duration36.1 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

appearance_id
Text

Unique 

Distinct1696240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size114.2 MiB
2025-03-12T21:11:11.234739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.575551
Min length10

Characters and Unicode

Total characters23027392
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1696240 ?
Unique (%)100.0%

Sample

1st row2231978_38004
2nd row2233748_79232
3rd row2234413_42792
4th row2234418_73333
5th row2234421_122011
ValueCountFrequency (%)
2231978_38004 1
 
< 0.1%
2235545_4582 1
 
< 0.1%
2234421_122011 1
 
< 0.1%
2234421_146889 1
 
< 0.1%
2235539_28716 1
 
< 0.1%
2235539_69445 1
 
< 0.1%
2235545_19409 1
 
< 0.1%
2235545_30003 1
 
< 0.1%
2235545_30667 1
 
< 0.1%
2235545_34129 1
 
< 0.1%
Other values (1696230) 1696230
> 99.9%
2025-03-12T21:11:11.894958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3089752
13.4%
3 2757615
12.0%
4 2296835
10.0%
1 2189354
9.5%
8 1942858
8.4%
5 1926692
8.4%
9 1811798
7.9%
0 1806407
7.8%
6 1786878
7.8%
7 1722963
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23027392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3089752
13.4%
3 2757615
12.0%
4 2296835
10.0%
1 2189354
9.5%
8 1942858
8.4%
5 1926692
8.4%
9 1811798
7.9%
0 1806407
7.8%
6 1786878
7.8%
7 1722963
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23027392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3089752
13.4%
3 2757615
12.0%
4 2296835
10.0%
1 2189354
9.5%
8 1942858
8.4%
5 1926692
8.4%
9 1811798
7.9%
0 1806407
7.8%
6 1786878
7.8%
7 1722963
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23027392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3089752
13.4%
3 2757615
12.0%
4 2296835
10.0%
1 2189354
9.5%
8 1942858
8.4%
5 1926692
8.4%
9 1811798
7.9%
0 1806407
7.8%
6 1786878
7.8%
7 1722963
7.5%

game_id
Real number (ℝ)

High correlation 

Distinct66489
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3158399.9
Minimum2211607
Maximum4570633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:11.968462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2211607
5-th percentile2250206
Q12588911
median3080230
Q33615124
95-th percentile4367902
Maximum4570633
Range2359026
Interquartile range (IQR)1026213

Descriptive statistics

Standard deviation659964.01
Coefficient of variation (CV)0.20895518
Kurtosis-1.079163
Mean3158399.9
Median Absolute Deviation (MAD)522868
Skewness0.33004758
Sum5.3574042 × 1012
Variance4.355525 × 1011
MonotonicityNot monotonic
2025-03-12T21:11:12.030583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2884392 36
 
< 0.1%
4098010 35
 
< 0.1%
4545866 35
 
< 0.1%
2740103 34
 
< 0.1%
4041615 34
 
< 0.1%
4002937 34
 
< 0.1%
4243699 34
 
< 0.1%
4243720 34
 
< 0.1%
4547624 34
 
< 0.1%
4148218 34
 
< 0.1%
Other values (66479) 1695896
> 99.9%
ValueCountFrequency (%)
2211607 27
< 0.1%
2218677 28
< 0.1%
2219794 28
< 0.1%
2219795 28
< 0.1%
2221641 14
< 0.1%
2221747 1
 
< 0.1%
2221748 1
 
< 0.1%
2221749 14
< 0.1%
2221751 14
< 0.1%
2221753 14
< 0.1%
ValueCountFrequency (%)
4570633 27
< 0.1%
4562600 32
< 0.1%
4562599 31
< 0.1%
4562598 32
< 0.1%
4562597 14
< 0.1%
4562596 30
< 0.1%
4562595 17
< 0.1%
4562593 14
< 0.1%
4559306 32
< 0.1%
4559305 29
< 0.1%

player_id
Real number (ℝ)

High correlation 

Distinct25664
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208378.94
Minimum10
Maximum1380876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:12.091526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15452
Q158252
median148396
Q3300168
95-th percentile603149
Maximum1380876
Range1380866
Interquartile range (IQR)241916

Descriptive statistics

Standard deviation192896.79
Coefficient of variation (CV)0.92570193
Kurtosis2.1678302
Mean208378.94
Median Absolute Deviation (MAD)103452
Skewness1.4389129
Sum3.534607 × 1011
Variance3.7209171 × 1010
MonotonicityNot monotonic
2025-03-12T21:11:12.154735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38253 605
 
< 0.1%
32467 578
 
< 0.1%
59561 563
 
< 0.1%
125781 563
 
< 0.1%
74229 560
 
< 0.1%
36139 558
 
< 0.1%
28396 558
 
< 0.1%
56416 551
 
< 0.1%
91845 549
 
< 0.1%
65278 545
 
< 0.1%
Other values (25654) 1690610
99.7%
ValueCountFrequency (%)
10 136
< 0.1%
26 152
< 0.1%
65 122
< 0.1%
77 4
 
< 0.1%
80 12
 
< 0.1%
109 41
 
< 0.1%
123 7
 
< 0.1%
132 77
< 0.1%
215 109
< 0.1%
258 19
 
< 0.1%
ValueCountFrequency (%)
1380876 1
 
< 0.1%
1378362 1
 
< 0.1%
1358447 1
 
< 0.1%
1310513 3
 
< 0.1%
1309326 3
 
< 0.1%
1306851 8
< 0.1%
1302421 5
< 0.1%
1294052 11
< 0.1%
1294049 5
< 0.1%
1294048 2
 
< 0.1%

player_club_id
Real number (ℝ)

High correlation 

Distinct1064
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3124.6588
Minimum1
Maximum116786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:12.216641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1289
median826
Q32441
95-th percentile16239
Maximum116786
Range116785
Interquartile range (IQR)2152

Descriptive statistics

Standard deviation8249.8986
Coefficient of variation (CV)2.6402558
Kurtosis37.534466
Mean3124.6588
Median Absolute Deviation (MAD)626
Skewness5.5673787
Sum5.3001712 × 109
Variance68060826
MonotonicityNot monotonic
2025-03-12T21:11:12.281831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
418 10257
 
0.6%
131 10166
 
0.6%
368 9983
 
0.6%
506 9644
 
0.6%
985 9631
 
0.6%
281 9571
 
0.6%
631 9554
 
0.6%
31 9443
 
0.6%
371 9358
 
0.6%
27 9324
 
0.5%
Other values (1054) 1599309
94.3%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 13
 
< 0.1%
3 4896
0.3%
4 1501
 
0.1%
5 8964
0.5%
6 467
 
< 0.1%
9 4
 
< 0.1%
10 1087
 
0.1%
11 9290
0.5%
12 9022
0.5%
ValueCountFrequency (%)
116786 2
 
< 0.1%
110302 361
< 0.1%
101634 1
 
< 0.1%
101362 3
 
< 0.1%
99200 2
 
< 0.1%
91328 3
 
< 0.1%
87883 1
 
< 0.1%
86209 285
< 0.1%
85465 327
< 0.1%
83678 445
< 0.1%

player_current_club_id
Real number (ℝ)

High correlation 

Distinct438
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3979.587
Minimum-1
Maximum110302
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size12.9 MiB
2025-03-12T21:11:12.345206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile29
Q1331
median903
Q32696
95-th percentile19771
Maximum110302
Range110303
Interquartile range (IQR)2365

Descriptive statistics

Standard deviation10806.832
Coefficient of variation (CV)2.7155662
Kurtosis30.513925
Mean3979.587
Median Absolute Deviation (MAD)713
Skewness5.1000201
Sum6.7503347 × 109
Variance1.1678762 × 108
MonotonicityNot monotonic
2025-03-12T21:11:12.406780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 11702
 
0.7%
683 10785
 
0.6%
1091 10266
 
0.6%
150 10255
 
0.6%
46 10109
 
0.6%
141 10108
 
0.6%
13 9981
 
0.6%
2381 9958
 
0.6%
368 9628
 
0.6%
589 9498
 
0.6%
Other values (428) 1593950
94.0%
ValueCountFrequency (%)
-1 6
 
< 0.1%
3 4765
0.3%
4 987
 
0.1%
5 8638
0.5%
6 618
 
< 0.1%
10 1190
 
0.1%
11 7593
0.4%
12 8008
0.5%
13 9981
0.6%
15 7461
0.4%
ValueCountFrequency (%)
110302 2320
0.1%
86209 729
 
< 0.1%
85465 1892
 
0.1%
83678 769
 
< 0.1%
75231 824
 
< 0.1%
71985 1222
 
0.1%
68608 1977
0.1%
63007 2555
0.2%
61825 2392
0.1%
60949 4824
0.3%

date
Date

Distinct3717
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum2012-07-03 00:00:00
Maximum2025-03-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:11:12.466769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:12.532827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct25119
Distinct (%)1.5%
Missing6
Missing (%)< 0.1%
Memory size126.2 MiB
2025-03-12T21:11:12.675067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.473428
Min length2

Characters and Unicode

Total characters22854086
Distinct characters121
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2111 ?
Unique (%)0.1%

Sample

1st rowAurélien Joachim
2nd rowRuslan Abyshov
3rd rowSander Puri
4th rowVegar Hedenstad
5th rowMarkus Henriksen
ValueCountFrequency (%)
de 14398
 
0.4%
van 14237
 
0.4%
david 11680
 
0.3%
lucas 9957
 
0.3%
thomas 8444
 
0.3%
kevin 7908
 
0.2%
daniel 7764
 
0.2%
andré 7245
 
0.2%
pedro 7121
 
0.2%
joão 7084
 
0.2%
Other values (23832) 3280994
97.2%
2025-03-12T21:11:12.879083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

competition_id
Categorical

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.0 MiB
IT1
139849 
ES1
138147 
GB1
135091 
FR1
131229 
TR1
119614 
Other values (38)
1032310 

Length

Max length4
Median length3
Mean length2.932575
Min length2

Characters and Unicode

Total characters4974351
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLQ
2nd rowELQ
3rd rowELQ
4th rowELQ
5th rowELQ

Common Values

ValueCountFrequency (%)
IT1 139849
 
8.2%
ES1 138147
 
8.1%
GB1 135091
 
8.0%
FR1 131229
 
7.7%
TR1 119614
 
7.1%
L1 112441
 
6.6%
NL1 107748
 
6.4%
PO1 107305
 
6.3%
BE1 92929
 
5.5%
RU1 85204
 
5.0%
Other values (33) 526683
31.1%

Length

2025-03-12T21:11:12.949183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
it1 139849
 
8.2%
es1 138147
 
8.1%
gb1 135091
 
8.0%
fr1 131229
 
7.7%
tr1 119614
 
7.1%
l1 112441
 
6.6%
nl1 107748
 
6.4%
po1 107305
 
6.3%
be1 92929
 
5.5%
ru1 85204
 
5.0%
Other values (33) 526683
31.1%

Most occurring characters

ValueCountFrequency (%)
1 1451827
29.2%
R 532301
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183742
 
3.7%
P 176424
 
3.5%
Other values (10) 1006643
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4974351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532301
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183742
 
3.7%
P 176424
 
3.5%
Other values (10) 1006643
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4974351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532301
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183742
 
3.7%
P 176424
 
3.5%
Other values (10) 1006643
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4974351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532301
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183742
 
3.7%
P 176424
 
3.5%
Other values (10) 1006643
20.2%

yellow_cards
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.8 MiB
0
1453054 
1
236436 
2
 
6750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696240
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

Length

2025-03-12T21:11:13.002885image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:11:13.048615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1453054
85.7%
1 236436
 
13.9%
2 6750
 
0.4%

red_cards
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.8 MiB
0
1689826 
1
 
6414

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1696240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

Length

2025-03-12T21:11:13.097194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:11:13.141867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1696240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1689826
99.6%
1 6414
 
0.4%

goals
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095945149
Minimum0
Maximum6
Zeros1550722
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:13.180833image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33104245
Coefficient of variation (CV)3.4503302
Kurtosis17.871909
Mean0.095945149
Median Absolute Deviation (MAD)0
Skewness3.8743458
Sum162746
Variance0.1095891
MonotonicityNot monotonic
2025-03-12T21:11:13.227430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1550722
91.4%
1 130219
 
7.7%
2 13566
 
0.8%
3 1562
 
0.1%
4 147
 
< 0.1%
5 23
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1550722
91.4%
1 130219
 
7.7%
2 13566
 
0.8%
3 1562
 
0.1%
4 147
 
< 0.1%
5 23
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 23
 
< 0.1%
4 147
 
< 0.1%
3 1562
 
0.1%
2 13566
 
0.8%
1 130219
 
7.7%
0 1550722
91.4%

assists
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075449818
Minimum0
Maximum6
Zeros1577536
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:13.272509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28542358
Coefficient of variation (CV)3.7829591
Kurtosis18.381635
Mean0.075449818
Median Absolute Deviation (MAD)0
Skewness4.0511378
Sum127981
Variance0.081466619
MonotonicityNot monotonic
2025-03-12T21:11:13.317580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1577536
93.0%
1 110036
 
6.5%
2 8100
 
0.5%
3 530
 
< 0.1%
4 36
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1577536
93.0%
1 110036
 
6.5%
2 8100
 
0.5%
3 530
 
< 0.1%
4 36
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 1
 
< 0.1%
4 36
 
< 0.1%
3 530
 
< 0.1%
2 8100
 
0.5%
1 110036
 
6.5%
0 1577536
93.0%

minutes_played
Real number (ℝ)

Distinct122
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.08907
Minimum1
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:11:13.373761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q145
median90
Q390
95-th percentile90
Maximum148
Range147
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.981579
Coefficient of variation (CV)0.43395546
Kurtosis-0.38137054
Mean69.08907
Median Absolute Deviation (MAD)0
Skewness-1.0701779
Sum1.1719164 × 108
Variance898.89508
MonotonicityNot monotonic
2025-03-12T21:11:13.434704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 907180
53.5%
45 66235
 
3.9%
1 26371
 
1.6%
78 11748
 
0.7%
12 11618
 
0.7%
76 11593
 
0.7%
77 11549
 
0.7%
14 11507
 
0.7%
75 11498
 
0.7%
74 11495
 
0.7%
Other values (112) 615446
36.3%
ValueCountFrequency (%)
1 26371
1.6%
2 7796
 
0.5%
3 8545
 
0.5%
4 9092
 
0.5%
5 9663
 
0.6%
6 10271
 
0.6%
7 10756
0.6%
8 11084
0.7%
9 11253
0.7%
10 11241
0.7%
ValueCountFrequency (%)
148 1
 
< 0.1%
135 2
 
< 0.1%
120 8143
0.5%
119 30
 
< 0.1%
118 30
 
< 0.1%
117 25
 
< 0.1%
116 21
 
< 0.1%
115 43
 
< 0.1%
114 28
 
< 0.1%
113 41
 
< 0.1%

Interactions

2025-03-12T21:11:06.602035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:00.904077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.931382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.833368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.678900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.513541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:05.687820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.727765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.088634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.060229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.952443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.791292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.626900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:05.827870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.850363image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.242255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.201137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.076041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.914280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.756923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:05.946786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.971644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.414711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.333721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.197728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.030145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.870918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.113053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:07.098219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.544741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.460549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.318018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.154504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.978069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.243376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:07.226151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.667250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.581527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.440983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.269642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:05.098231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.352306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:07.347365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:01.791366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:02.704026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:03.559428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:04.393624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:05.451983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:11:06.463843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-12T21:11:13.480144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
assistscompetition_idgame_idgoalsminutes_playedplayer_club_idplayer_current_club_idplayer_idred_cardsyellow_cards
assists1.0000.016-0.0100.0720.055-0.035-0.035-0.0090.0070.005
competition_id0.0161.0000.1140.0160.0880.1660.1930.0690.0160.045
game_id-0.0100.1141.000-0.006-0.1110.028-0.0430.6400.0070.016
goals0.0720.016-0.0061.0000.050-0.031-0.036-0.0060.0090.006
minutes_played0.0550.088-0.1110.0501.000-0.003-0.024-0.1620.0620.098
player_club_id-0.0350.1660.028-0.031-0.0031.0000.6180.0800.0020.008
player_current_club_id-0.0350.193-0.043-0.036-0.0240.6181.0000.0010.0010.007
player_id-0.0090.0690.640-0.006-0.1620.0800.0011.0000.0030.013
red_cards0.0070.0160.0070.0090.0620.0020.0010.0031.0000.012
yellow_cards0.0050.0450.0160.0060.0980.0080.0070.0130.0121.000

Missing values

2025-03-12T21:11:07.575658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T21:11:08.565020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

appearance_idgame_idplayer_idplayer_club_idplayer_current_club_iddateplayer_namecompetition_idyellow_cardsred_cardsgoalsassistsminutes_played
02231978_380042231978380048532352012-07-03Aurélien JoachimCLQ002090
12233748_79232223374879232884126982012-07-05Ruslan AbyshovELQ000090
22234413_4279222344134279262514652012-07-05Sander PuriELQ000045
32234418_73333223441873333127466462012-07-05Vegar HedenstadELQ000090
42234421_122011223442112201119530082012-07-05Markus HenriksenELQ000190
52234421_146889223442114688919527782012-07-05Peter AnkersenELQ100090
62235539_2871622355392871628271852012-07-05Adi AdilovicELQ000090
72235539_69445223553969445282197712012-07-05Ivan SesarELQ100190
82235545_194092235545194093172002012-07-05Willem JanssenELQ000045
92235545_300032235545300033173172012-07-05Wout BramaELQ000090
appearance_idgame_idplayer_idplayer_club_idplayer_current_club_iddateplayer_namecompetition_idyellow_cardsred_cardsgoalsassistsminutes_played
16962304556277_3421514556277342151257825782025-03-10Daniels BalodisSFA000090
16962314556277_3591994556277359199257825782025-03-10Andy FisherSFA000090
16962324556277_4309764556277430976257825782025-03-10Jonathan SvedbergSFA000069
16962334556277_5037314556277503731257825782025-03-10Stephen Duke-McKennaSFA100045
16962344556277_7001444556277700144257825782025-03-10Makenzie KirkSFA000090
16962354556277_7412674556277741267257825782025-03-10Sam CurtisSFA000090
16962364556277_7963074556277796307257825782025-03-10Zach MitchellSFA000090
16962374556277_80339455627780339257825782025-03-10Graham CareySFA001021
16962384556277_90925445562779092541241432025-03-10Macaulay TaitSFA000090
16962394556277_91854455627791854257825782025-03-10Barry DouglasSFA000090